AI Bootcamp for Non-Technical Investors: Build Your Own Investment Idea Engine
A self-paced, 16-lesson series for investors who want to use AI well, on their own terms.
Active investing using AI tools well is a real edge, but only for investors who learn to use them in a way that respects their own philosophy. Generic AI outputs are not enough. Pasted into a research process, they are at best a distraction and at worst a quiet source of error.
The AI Bootcamp for Non-Technical Investors is a 16-lesson series, plus an introduction, that walks you through building your own AI-powered investment idea engine. No coding background is required. No statistics background is required. The only prerequisites are a working investment philosophy, the patience to verify what the machine tells you, and roughly 30 minutes per lesson.
The bootcamp originally ran as a four-week cohort in May 2026. Everything is now published and available for you to work through at your own pace.
What you will actually build
By the end of the series, you will have, on your own machine and in your own accounts:
A “My Analyst” workspace in both Perplexity and Claude, with a system prompt that encodes your investing philosophy, your “never do this” rules, and your tone.
A small library of reusable prompt templates and a personal “always verify” checklist that sits between any AI output and a buy, sell, or pass decision.
A working data layer built on three free or low-cost backbones: SEC EDGAR for primary filings, FRED for macro and sector data, and FMP for fundamentals — with a bench of free fallbacks behind them.
An idea-record JSON schema and a wide-research workflow that lets you triage 20 companies in an afternoon.
A real Python screener, version-controlled in Git and GitHub, that pulls from FMP, layers on EDGAR filing freshness and FRED macro context, scores survivors against weights you wrote down, and produces a narrative triage pack on the top three names.
A scheduled, automated end-to-end run of that engine, delivered to a channel you choose, with a README and a three-month retrospective that hold future-you accountable.
You will end the bootcamp with a working system you understand line by line, not a black box.
Who this is for
Value investors who want AI in their workflow without surrendering judgment.
Non-technical investors who have been put off by tutorials written for engineers.
MOI members and Latticework subscribers who already have a philosophy and want a faster, more disciplined research loop around it.
If you can read a 10-K and you can follow a recipe, you can complete this bootcamp.
What you will need
Perplexity Pro (~$20/month).
Claude Pro (~$20/month).
FMP, optional but recommended (~$15+/month).
Free accounts at GitHub, FRED, and SEC EDGAR.
That is the full stack. No Bloomberg terminal, no FactSet seat, no engineering team.
How the bootcamp is structured
The 16 lessons are organized in four phases. Each lesson takes roughly 30 minutes and produces a concrete artifact you keep.
Phase 1: Foundations (Lessons 1–3)
How AI models actually think, the prompt patterns that outperform casual prompting, and the agentic workflows that leave behind clean, reusable artifacts.
Introduction: Build Your Own Investment Idea Engine — The framing post. Why this series exists, who it is for, what you will build, and how to use it.
Lesson 1: How LLMs Work, and How to Defend Against Hallucinations — LLMs are fluent first, accurate second. The four defenses against hallucination, two hands-on tests against a real 10-Q, and your personal “always verify” checklist.
Lesson 2: Prompt Patterns That Outperform Casual Prompting — The five-element prompt template (Role, Task, Context, Constraints, Format), a reusable Buffett-style quality screen, and a “My Analyst” Space and Project with your philosophy baked in.
Lesson 3: Tools, Agents, and Structured Output — Three tiers of output reliability and a small idea-record JSON schema that will accumulate over the four weeks as a library of comparable records, not a pile of notes.
Phase 2: The data layer (Lessons 4–7)
Where investors get clean, free, reliable data, and how to pull it into the engine.
Lesson 4: SEC EDGAR, the Primary Source — Filing types, the EDGAR API, full-text search operators, a five-bullet risk summary from a real 10-K, and a personal list of EDGAR searches worth monitoring.
Lesson 5: Free Access to FRED Macro/Sector Data — A free FRED API key, a 10-series macro core (rates, inflation, labor, credit, money, production), and a sector layer for the industries you actually follow.
Lesson 6: FMP API Key and the First Checked Data Pull — Registering for FMP, pulling the three statements and key metrics for one ticker, and verifying one number against the company’s own filing on EDGAR.
Lesson 7: Other Data Sources, and Idea Engine Formats — A bench of free and low-cost fallbacks (Stock Analysis, Fiscal.ai, Koyfin, OpenInsider, WhaleWisdom, others) and the three formats the engine will speak: CSV, JSON, and Markdown.
Phase 3: Perplexity and Claude as research partners (Lessons 8–11)
Spaces, Projects, connectors, memory, sub-agents, and screening at scale.
Lesson 8: Spaces, Projects, and Connectors — Three durable research rooms (Watchlist Monitor, Quick Ideas, Deep Dive Workshop) across Perplexity and Claude, with connectors to Drive, Sheets, and GitHub. One room, one job.
Lesson 9: Memory, Sub-Agents, and Parallel Research — A memory audit, durable preferences vs. task instructions, and five sub-agents researching five companies along five dimensions in parallel — merged by you.
Lesson 10: Wide Research for Screening at Scale — Qualitative triage across 20 companies with one strict 11-column schema. Five sample universes, from quality compounders to recent spin-offs to distressed equity.
Lesson 11: Iterating Prompts and Structured Write-Ups — A prompt-evals sheet (five rows, three scores, one failure mode, one fix) and a reverse-DCF prompt you would actually trust.
Phase 4: Build and ship your engine (Lessons 12–16)
Claude Code as a terminal pair-programmer for non-coders, Git and GitHub as editable memory, and the screener, scoring, narrative, and schedule that make the whole thing real.
Lesson 12: Claude Code, and Building Our First Script — Installing Claude Code, working in Plan Mode, and writing a Python script that pulls the risk-factors section from a 10-K for any ticker. Every line explained in plain English before it runs.
Lesson 13: Git, GitHub, and a Real Screener on FMP — Version control as editable memory, a private GitHub repository, API keys stored safely in a .env file, and a first real screener against the FMP screener endpoint.
Lesson 14: Layer On EDGAR, FRED, and a Scoring Layer — Filing freshness from EDGAR, dated macro context from FRED, and a 0-to-100 scoring layer whose weights live in a YAML config file you can edit without touching the script.
Lesson 15: Engine Spec, Narrative Layer, and End-to-End Run — A one-page engine spec (nine questions the code cannot answer for itself), the five-box architecture (Data In → Filter → Score → Narrate → Deliver), and a narrative triage pack on the top three names.
Lesson 16: Schedule It, Refine It, and Retrospective — Scheduling the engine (GitHub Actions, Perplexity scheduled tasks, APScheduler, or local cron), choosing a delivery channel, a README for future-you, and a one-page three-month retrospective that holds you to your verification standard.
How to use the series
You do not have to do this in four weeks. You can do it in eight, or in four weekends, or one lesson per Sunday morning over a season. The lessons build on each other, so the order matters. Each lesson produces a concrete artifact you keep, so you can stop and start without losing your place.
A few suggestions:
Treat the artifacts as the deliverables, not the reading. The point of Lesson 2 is your “My Analyst” Space, not the post itself.
Verify before you trust. The “always verify” checklist from Lesson 1 is the through-line of the entire bootcamp.
Skip the coding lessons at your peril. Lessons 12 through 16 are where the engine becomes yours rather than a rented chatbot. Plan Mode in Claude Code is gentle on non-coders, and the lessons are designed for someone who has never opened a terminal.
Access
The Introduction and each lesson are partially accessible to everyone. Full access to the lessons, including all prompts, schemas, scripts, and walkthroughs, is included with a paid Latticework subscription or MOI membership.
Start with the Introduction, then work through the lessons in order. The engine you finish with is yours.